Unsupervised segmentation of an image into homogeneously
textured regions and the recognition of known texture patterns have
been important tasks in computer vision. This thesis presents a new
set of algorithms and describes an implemented system which performs
these tasks.
Initial features are computed from a local multi-channel spectral
decomposition of the image that is implemented with Gabor filters.
Textures are not assumed to have a band limited frequency spectrum
and there is no supposition regarding
the image contents: it may contain some unknown texture patterns or
regions with no textures at all. Stability of features is enhanced by
employing a method for smoothing reliable measurements.
Both recognition and segmentation procedures use robust statistical
algorithms and
are performed locally for small image patches.
In particular, statistical classification
with principal components is used for recognition.
Further accuracy is achieved by employing spatial consistency constraints.
When a slanted texture is projected on the image plane, the patterns
undergo systematic changes in the density, area, and directionality of
the texture elements.
Recognition is made invariant to such
transformations by representing texture classes with multiple
descriptors. These descriptors are computed from carefully selected
3-D views of the patterns.
Simulated projection of textures from arbitrary viewpoints are obtained
by using a new texture mapping algorithm.
The segmentation algorithm overcomes the non-stationarity of the features by
employing a new, robust similarity measure.
The performance of these methods is demonstrated by
applying them to real images.